{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import gensim\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.15.4'"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.7.0'"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gensim.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load pretrained bert token vectors and project them to 2d space using tSNE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = \"bert-base-uncased.30522.768d.vec\"\n",
    "# f = 'bert-base-cased.28996.768d.vec'\n",
    "# f = 'bert-base-multilingual-uncased.105879.768d.vec'\n",
    "# f = 'bert-base-chinese.21128.768d.vec'\n",
    "# f = 'bert-base-multilingual-cased.119547.768d.vec'\n",
    "# f = 'bert-base-uncased.30522.768d.vec'\n",
    "# f = 'bert-large-cased.28996.1024d.vec'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = gensim.models.KeyedVectors.load_word2vec_format(f, binary=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find most related tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('feel', 0.4074964225292206),\n",
       " ('style', 0.3370037078857422),\n",
       " ('get', 0.32385802268981934),\n",
       " ('think', 0.3120831251144409),\n",
       " ('work', 0.3055831491947174),\n",
       " ('pose', 0.2996072769165039),\n",
       " ('point', 0.2994779348373413),\n",
       " ('follow', 0.29945236444473267),\n",
       " ('fashion', 0.29616934061050415),\n",
       " ('eyes', 0.2930969297885895)]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar(\"look\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def tsne(query, topn=10):\n",
    "    results = model.wv.similar_by_word(query, topn=topn)\n",
    "    words = [query]+[r[0] for r in results]\n",
    "    wordvectors = np.array(model[query]+[model[w] for w in words], np.float32)\n",
    "    reduced = TSNE(n_components=2).fit_transform(wordvectors)\n",
    "    plt.figure(figsize=(20, 20), dpi=100)\n",
    "    max_x = np.amax(reduced, axis=0)[0]\n",
    "    max_y = np.amax(reduced, axis=0)[1]\n",
    "    plt.xlim((-max_x, max_x))\n",
    "    plt.ylim((-max_y, max_y))\n",
    "    plt.scatter(reduced[:, 0], reduced[:, 1], s=20, c=[\"r\"] + [\"b\"]*(len(reduced)-1))\n",
    "    \n",
    "    for i in range(len(words)):\n",
    "        target_word = words[i]\n",
    "        # print(target_word)\n",
    "        x = reduced[i, 0]\n",
    "        y = reduced[i, 1]\n",
    "        plt.annotate(target_word, (x, y))\n",
    "    plt.axis('off')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ryan/pytorch1.0/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne(\"look\", 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ryan/pytorch1.0/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne(\"##go\", 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
